Abstract:

[Purpose/Significance] With the rapid advancement of generative artificial intelligence, open-source agents have emerged as a key driving force in reshaping the development paradigm of artificial intelligence. These agents integrate foundation models, tool chains, and collaborative mechanisms to enable autonomous task execution. From the perspective of technological evolution, this paper conducts a systematic analysis of three core issues concerning open-source agents: technological evolution, application scenarios, and security governance, aiming to clarify the evolutionary laws of open-source agents, expand their application boundaries, and provide theoretical and practical references for their standardized development and rational application. [Method/Process] In terms of evolutionary stages, this study proposed that open-source agents have undergone four distinct phases: pre-history of technology (primitive tool integration stage), single-point intelligence (independent task execution stage), systemic intelligence (multi-tool collaborative stage), and ecological intelligence (multi-agent symbiotic stage). This evolution was driven by a shift in focus from relying solely on the capabilities of foundation models to gradually forming a mature agent ecosystem featuring autonomous operation, multi-agent collaboration mechanisms, and cross-platform interoperability. As a prime example of a practice in the ecological intelligence stage, OpenClaw, with its open-source architecture, modular design, and multi-agent collaborative capabilities, marked a paradigm shift in artificial intelligence, moving from "Model as a Service" (MaaS) to "Agent as a Service" (AaaS) and enabling end-to-end task closed-loop management. Regarding application dimensions, a three-tier progressive scenario framework was constructed, encompassing knowledge-intensive assistance (such as academic research and intelligent consulting), tool-intensive execution (such as automated office and industrial control), and collaboration-intensive processes (such as public governance and team collaboration), emphasizing that its core value lies in accomplishing complex end-to-end tasks in a verifiable, auditable, and sustainable manner. In the governance dimension, a four-layer embedded governance framework was proposed to address the security risks and regulatory challenges posed by open-source agents. This framework covers the protocol layer (standard formulation), the platform layer (technical supervision), the execution layer (behavioral constraints), and the ecological layer (industry self-discipline). [Results/Conclusions] This study found that the competitive focus of open-source agents has gradually shifted from the performance of individual models to ecological controllability and governance credibility. As a new type of intelligent entity, whether open-source agents can effectively support diverse application scenarios, such as scientific research, public governance, and industrial production, fundamentally depends on their ability to realize the deep integration of advanced technological capabilities and sound institutional trust. This also provides a core direction for the future development and governance of open-source agents.

Key words: open-source agents, OpenClaw, evolutionary development, application scenarios, security governance, generative artificial intelligence, skill